U.S. patent application number 17/040229 was filed with the patent office on 2021-01-14 for intelligent bionic human body part model detection device and method for manufacturing same.
This patent application is currently assigned to The Hong Kong Research Institute of Textiles and Apparel Limited. The applicant listed for this patent is The Hong Kong Research Institute of Textiles and Apparel Limited. Invention is credited to Xia GUO, Shi-rui LIU, Su LIU, Xiao-ming TAO, Xi WANG, Bao YANG.
Application Number | 20210010877 17/040229 |
Document ID | / |
Family ID | 1000005153343 |
Filed Date | 2021-01-14 |
United States Patent
Application |
20210010877 |
Kind Code |
A1 |
TAO; Xiao-ming ; et
al. |
January 14, 2021 |
INTELLIGENT BIONIC HUMAN BODY PART MODEL DETECTION DEVICE AND
METHOD FOR MANUFACTURING SAME
Abstract
Disclosed are an intelligent bionic human body part model
detection device and a method for manufacturing same. The device
comprises: a bionic human body part model (1); and multiple optical
fiber grating sensing units (5) which are integrated on an optical
fibre and arranged at multiple pre-determined positions of the
bionic human body part model (1). The device can improve the
accuracy of the detection of pressure applied to the intelligent
bionic human body part model.
Inventors: |
TAO; Xiao-ming; (Hong Kong,
CN) ; YANG; Bao; (Hong Kong, CN) ; WANG;
Xi; (Hong Kong, CN) ; LIU; Su; (Hong Kong,
CN) ; GUO; Xia; (Hong Kong, CN) ; LIU;
Shi-rui; (Hong Kong, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Hong Kong Research Institute of Textiles and Apparel
Limited |
Hong Kong |
|
CN |
|
|
Assignee: |
The Hong Kong Research Institute of
Textiles and Apparel Limited
Hong Kong
CN
|
Family ID: |
1000005153343 |
Appl. No.: |
17/040229 |
Filed: |
March 29, 2018 |
PCT Filed: |
March 29, 2018 |
PCT NO: |
PCT/CN2018/081017 |
371 Date: |
September 22, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/62 20170101; G02B
6/02076 20130101; G01L 1/246 20130101; G06T 2207/30196 20130101;
G06T 2200/04 20130101 |
International
Class: |
G01L 1/24 20060101
G01L001/24; G06T 7/62 20060101 G06T007/62; G02B 6/02 20060101
G02B006/02 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 22, 2018 |
CN |
2018102493228 |
Claims
1. A detection device of intelligent bionic human body part model,
comprising: a bionic human body part model; and a plurality of
fiber grating sensing units integrated on an optical fiber and a
plurality of predetermined positions set on the bionic human body
part model.
2. The detection device according to claim 1, wherein each of the
plurality of fiber grating sensing units comprises: a substrate; a
groove on the substrate; and a Bragg grating, suspended on a
surface of the groove, wherein an air chamber is formed at the
groove after packaging.
3. The detection device according to claim 1, wherein a plurality
of optical fiber sensing channels are set on the bionic human body
part model, the plurality of optical fiber grating sensing units
are dispersed in each of the optical fiber sensing channels, at
least one fiber grating sensing unit is dispersed in one fiber
optic sensing channel, and the at least one fiber grating sensing
unit has different center wavelengths.
4. The detection device according to claim 1, wherein material of
the bionic human body part model is an elastomer with a Young's
modulus below 1 MPa.
5. The detection device according to claim 4, wherein the elastomer
with a Young's modulus below 1 MPa is one or more of following:
silicone, polyurethane, polyester, and polyacrylic acid.
6. The detection device according to claim 1, further comprising: a
base set at a bottom of the bionic human body part model; a
protective layer integrally connected to an upper surface of the
base for protecting the optical fiber.
7. The detection device according to claim 1, wherein the bionic
human body part model is generated as following: intercepting a
plurality of cross-sections for each human body part sample among a
plurality of human body part samples; selecting a plurality of
parameters on each cross-section, the plurality of parameters
uniquely defining the cross-section; constructing a parameter
matrix using a plurality of parameters of a plurality of
intercepted cross-sections for each human body part sample of the
plurality of human body part samples; determining an average
parameter matrix and a principal component vector for the parameter
matrices of the plurality of human body part samples, wherein the
average parameter matrix represents an average body part shape and
the principal component vector represents a main difference of the
human body part; and generating the bionic human body part model
based on an average parameter matrix, a principal component vector,
and at least one principal component measurement value on a target
human body part.
8. A method for manufacturing the detection device of intelligent
bionic human body part model according to claim 1, comprises:
intercepting a plurality of cross-sections for each human body part
sample among a plurality of human body part samples; selecting a
plurality of parameters on each cross-section, the plurality of
parameters uniquely defining the cross-section; constructing a
parameter matrix using a plurality of parameters of a plurality of
intercepted cross-sections for each human body part sample of the
plurality of human body part samples; determining an average
parameter matrix and a principal component vector for the parameter
matrices of the plurality of human body part samples, wherein the
average parameter matrix represents an average body part shape and
the principal component vector represents a main difference of the
human body part; and generating the bionic human body part model
based on an average parameter matrix, a principal component vector,
and at least one principal component measurement value on a target
human body part.
9. The method according to claim 8, wherein the generating the
bionic human body part model based on the average parameter matrix,
the principal component vector, and at least one principal
component measurement value on the target human body part,
specifically comprises: generating an average bionic human body
part model based on the average parameter matrix; and deforming the
average bionic human body part model according to at least one
principal component measurement value on the target human body part
and corresponding principal component value in the principal
component vector.
10. The method according to claim 9, wherein the deforming the
average bionic human body part model according to at least one
principal component measurement value on the target human body part
and corresponding principal component value in the principal
component vector, specifically comprises: determining a ratio of at
least one principal component measurement value on the target human
body part and the corresponding principal component value in the
principal component vector; and deforming the average bionic human
body part model according to product of the ratio and the principal
component vector.
11. The method according to claim 8, wherein before the generating
the bionic human body part model based on the average parameter
matrix, the principal component vector, and at least one principal
component measurement value on the target human body part, the
method further comprises: obtaining a three-dimensional image of
the target human body part; and obtaining at least one principal
component measurement value on the target human body part from the
three-dimensional image.
12. The method according to claim 8, wherein the determining the
principal component vector for the parameter matrices of the
plurality of human body part samples specifically comprises:
obtaining the variance matrix for the parameter matrices of the
multiple human body part samples; and determining the principal
component vector based on the variance matrix.
13. The method according to claim 8, wherein material used for
generating the bionic human body part model comprises an elastomer
with a Young's modulus below 1 MPa.
14. The method according to claim 13, wherein the elastomer with a
Young's modulus below 1 MPa is one or more of following: silicone,
polyurethane, polyester, and polyacrylic acid.
15. The method according to claim 8, wherein after the generating
the bionic human body part model based on an average parameter
matrix, the method further comprises: setting a plurality of fiber
grating sensing units integrated on an optical fiber at a plurality
of predetermined positions of the bionic human body part model.
16. The method according to claim 8, wherein after the generating
the bionic human body part model based on an average parameter
matrix, the method further comprises: setting a base at the bottom
of the bionic human body part model; and integrally connecting a
protective layer to an upper surface of the base for protecting the
optical fiber.
17. The detection device according to claim 8, wherein each of the
plurality of fiber grating sensing units is constructed as follows:
setting a substrate; etching a groove on the substrate; and
suspending a Bragg grating on a surface of the groove, wherein an
air chamber is formed at the groove after packaging.
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the field of body pressure
measurement, and in particular to an intelligent pressure sensing
bionic human body part model device and a manufacturing method
thereof.
BACKGROUND
[0002] The fit of clothing is very important for a pressure
garment. The pressure garment forms suitable pressure on
capillaries of human body and regulates blood circulation of the
human body. Medical research shows that applying reasonable
pressure can help prevent appearance of venous diseases (such as
edema, phlebitis, thrombosis) and prevent further development of
venous diseases. The pressure exerted by compression stockings on
human legs largely depends on the localized leg shape and
mechanical properties. In addition, the pressure garment or
wearable devices can also be used to enhance exercise effects,
reduce fatigue and injuries. Applying reasonable pressure is the
key to positive effect of the pressure garment. Therefore, when
manufacturing or applying the pressure garment, it is particularly
important to accurately measure the pressure exerted on the human
body. For example, in the design of compression stockings, pressure
at the ankle should be maximized and gradually decrease along the
leg. By squeezing blood vessels and muscles on the surface of the
leg, part of the blood in these blood vessels is squeezed to other
veins such as deeper veins, which helps more blood flow back to the
heart and also reduces the amount of blood trapped in the legs.
[0003] The related methods for measuring pressure of the pressure
garment on the human body can be divided into two categories
according to whether it directly acts on the human body: a direct
measurement type and an indirect measurement type. The measurement
method of the direct measurement type may use pneumatic,
piezoresistive, piezoelectric, capacitive sensors or sensor arrays
to measure directly on the surface of the human body. These sensors
or sensor arrays are large in size and located under the
compression stockings, which will deform both the compression
stockings and the legs, and it is impossible to measure true
pressure exerted by the compression stockings on the human legs. In
addition, the shape of the human legs changes slightly from morning
to night. Therefore, the repeatability of these methods is very
poor, and the measurement results are not accurate. The measurement
method of indirect measurement type uses a human body part model to
simulate a human body part, for example, using a human leg model to
simulate a human leg. Then, electronic pressure sensors are set at
different positions on the human body part model to measure the
pressure applied by the compression stockings to the human body
part model. However, the electronic pressure sensor has low
detection signal accuracy, poor immunity to electromagnetic
interference, slow measurement speed, poor reusability, small
information capacity, large weight and volume, and is not durable.
Moreover, the signal change of the electronic pressure sensor under
an action of small pressure is not obvious, and its sensing
response is the same order of magnitude as the noise generated by
the environment or even smaller, so it is seriously interfered by
external electromagnetic signals.
[0004] In addition, when constructing the human body part model,
MST professional system in related arts uses a rigid human body
part model. However, the pressure exerted by compression stockings
on the human body is not uniform even in a cross-section with the
same height, because the cross-sectional curvature varies with
different positions, there are bone protrusions in some positions,
and different tissues have different mechanical properties. In
short, the related measurement methods may not accurately measure
the pressure exerted by compression stockings on the human body, or
may only measure the pressure between the compression stockings and
the sensor embedded in the fixed and rigid human body part model,
or the measured pressure is based on the assumption of an
unreasonable cross-section. The measurement results of compression
stockings do not match the actual pressure applied to the human
leg, which will lead to excessive or insufficient pressure applied
to the human body, causing discomfort to the user, failing to
achieve the preset effect or even causing serious problems such as
local ulceration.
[0005] In addition, there are obvious differences in human
appearance. If considering differences in race, gender, age, etc.,
a large number of models of different sizes are required.
Therefore, it is very worthwhile to build a deformable bionic
model. The model has high measurement accuracy and can effectively
reduce the difference between the test results of the pressure
garment and the actual pressure applied to the human body.
SUMMARY
[0006] An object of the present disclosure is to provide a
detection device of intelligent bionic human body part model
capable of improving the accuracy of detecting applied
pressure.
[0007] According to a first aspect of embodiments in the present
disclosure, there is provided a detection device of intelligent
bionic human body part model, including:
[0008] a bionic human body part model; and
[0009] a plurality of fiber grating sensing units integrated on an
optical fiber and a plurality of predetermined positions set on the
bionic human body part model.
[0010] In an embodiment, each of the plurality of fiber grating
sensing units includes:
[0011] a substrate;
[0012] a groove on the substrate; and
[0013] a Bragg grating, suspended on a surface of the groove,
wherein an air chamber is formed at the groove after packaging.
[0014] In an embodiment, a plurality of optical fiber sensing
channels are set on the bionic human body part model, the plurality
of optical fiber grating sensing units are dispersed in each of the
optical fiber sensing channels, at least one fiber grating sensing
unit is dispersed in one fiber optic sensing channel, and at least
one fiber grating sensing unit has different center
wavelengths.
[0015] In an embodiment, material of the bionic human body part
model is an elastomer with a Young's modulus below 1 MPa.
[0016] In an embodiment, the elastomer with a Young's modulus below
1 MPa is one or more of following:
[0017] silicone, polyurethane, polyester, and polyacrylic acid.
[0018] In an embodiment, the detection device further includes:
[0019] a base set at a bottom of the bionic human body part
model;
[0020] a protective layer integrally connected to an upper surface
of the base for protecting the optical fiber.
[0021] In an embodiment, the bionic human body part model is
generated as following: intercepting a plurality of cross-sections
for each human body part sample among a plurality of human body
part samples; selecting a plurality of parameters on each
cross-section, the plurality of parameters uniquely defining the
cross-section; constructing a parameter matrix using a plurality of
parameters of a plurality of intercepted cross-sections for each
human body part sample of the plurality of human body part samples;
determining an average parameter matrix and a principal component
vector for the parameter matrices of the plurality of human body
part samples, wherein the average parameter matrix represents an
average body part shape and the principal component vector
represents a main difference of the human body part; and generating
the bionic human body part model based on an average parameter
matrix, a principal component vector, and at least one principal
component measurement value on a target human body part.
[0022] According to a second aspect of embodiments in the present
disclosure, there is also provided a method for manufacturing the
above-mentioned detection device of intelligent bionic human body
part model, including:
[0023] intercepting a plurality of cross-sections for each human
body part sample among a plurality of human body part samples;
[0024] selecting a plurality of parameters on each cross-section,
the plurality of parameters uniquely defining the
cross-section;
[0025] constructing a parameter matrix using a plurality of
parameters of a plurality of intercepted cross-sections for each
human body part sample of the plurality of human body part
samples;
[0026] determining an average parameter matrix and a principal
component vector for the parameter matrices of the plurality of
human body part samples, wherein the average parameter matrix
represents an average body part shape and the principal component
vector represents a main difference of the human body part; and
[0027] generating the bionic human body part model based on the
average parameter matrix, the principal component vector, and at
least one principal component measurement value on a target human
body part.
[0028] In an embodiment, the generating the bionic human body part
model based on the average parameter matrix, the principal
component vector, and at least one principal component measurement
value on the target human body part, specifically includes:
[0029] generating an average bionic human body part model based on
the average parameter matrix; and
[0030] deforming the average bionic human body part model according
to at least one principal component measurement value on the target
human body part and corresponding principal component value in the
principal component vector.
[0031] In an embodiment, deforming the average bionic human body
part model according to at least one principal component
measurement value on the target human body part and corresponding
principal component value in the principal component vector,
specifically includes:
[0032] determining a ratio of at least one principal component
measurement value on the target human body part and the
corresponding principal component value in the principal component
vector; and
[0033] deforming the average bionic human body part model according
to product of the ratio and the principal component vector.
[0034] In an embodiment, before the generating the bionic human
body part model based on the average parameter matrix, the
principal component vector, and at least one principal component
measurement value on the target human body part, the method further
includes:
[0035] obtaining a three-dimensional image of the target human body
part; and
[0036] obtaining at least one principal component measurement value
on the target human body part from the three-dimensional image.
[0037] In an embodiment, the determining the principal component
vector for the parameter matrices of the plurality of human body
part samples specifically includes:
[0038] obtaining the variance matrix for the parameter matrices of
the multiple human body part samples; and
[0039] determining the principal component vector based on the
variance matrix.
[0040] In an embodiment, material used for generating the bionic
human body part model includes an elastomer with a Young's modulus
below 1 MPa.
[0041] In an embodiment, the elastomer with a Young's modulus below
1 MPa is one or more of following:
[0042] silicone, polyurethane, polyester, and polyacrylic acid.
[0043] In an embodiment, after the generating the bionic human body
part model based on an average parameter matrix, the method further
includes: setting a plurality of fiber grating sensing units
integrated on an optical fiber at a plurality of predetermined
positions of the bionic human body part model.
[0044] In an embodiment, after the generating the bionic human body
part model based on an average parameter matrix, the method further
includes:
[0045] setting a base at the bottom of the bionic human body part
model; and
[0046] integrally connecting a protective layer to an upper surface
of the base for protecting the optical fiber.
[0047] In an embodiment, each of the plurality of fiber grating
sensing units is constructed as follows:
[0048] setting a substrate;
[0049] etching a groove on the substrate; and
[0050] suspending a Bragg grating on a surface of the groove,
wherein an air chamber is formed at the groove after packaging.
[0051] In embodiments of the present disclosure, the fiber grating
sensing unit is set at the plurality of predetermined positions on
the bionic human body part model, instead of setting the electronic
pressure sensor on the bionic human body part model, and these
fiber grating sensing units are integrated on the optical fiber,
that is, the fiber grating sensing network is adopted. The fiber
grating sensor network has the following advantages: high
measurement accuracy, strong anti-electromagnetic interference
ability, fast measurement speed, good reusability, and large
information capacity (one same fiber may transmit multiple
signals). In addition, the fiber grating sensing unit also has
characteristics of light weight, small size, insulation,
durability, long-term stability and so on. Moreover, sensing of
each fiber grating sensing unit is independent of each other, and
mutual interference is very small, which can effectively
inhibit/isolate the transmission of internal fiber deformation and
external fiber deformation of the fiber grating sensing unit. In
related art, an electronic pressure sensor is applied to the bionic
human body part model, the signal generated under small pressure
does not change significantly, and its sensing response is the same
order of magnitude as the noise generated by the environment or
even smaller, so it is seriously interfered by external signals.
The embodiments of the present disclosure adopt the fiber grating
sensor network, which can effectively avoid the problem.
[0052] Other characteristics and advantages of the present
disclosure will become apparent through the following detailed
description, or partly learned through the practice of the present
disclosure.
[0053] It should be understood that the above general description
and the following detailed description are merely exemplary, which
are not limited to the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0054] Exemplary embodiments of the present disclosure will be
described in detail below with reference to accompanying drawings,
the above and other objects, features, and advantages of the
present disclosure can become more apparent.
[0055] FIG. 1 is a flowchart of a method for manufacturing a
detection device of intelligent bionic human body part model
detection device according to an exemplary embodiment of the
present disclosure.
[0056] FIG. 2 is a specific flowchart of generating the bionic
human body part model based on an average parameter matrix, a
principal component vector, and at least one principal component
measurement value on a target human body part according to an
exemplary embodiment of the present disclosure.
[0057] FIG. 3 is a specific flowchart of deforming the average
bionic human body part model according to at least one principal
component measurement value on the target human body part and
corresponding principal component value in the principal component
vector according to an exemplary embodiment of the present
disclosure.
[0058] FIG. 4 is a flowchart of a method for manufacturing a
detection device of intelligent bionic human body part model
detection device according to an exemplary embodiment of the
present disclosure.
[0059] FIG. 5 is a specific flowchart of a method for constructing
a fiber grating sensing unit according to an exemplary embodiment
of the present disclosure.
[0060] FIG. 6 is a structural diagram of a detection device of
intelligent bionic human body part model according to an exemplary
embodiment of the present disclosure.
[0061] FIG. 7 is an exemplary structure of a part of the detection
device of intelligent bionic human body part model except for the
bionic human body part model according to an exemplary embodiment
of the present disclosure.
[0062] FIG. 8 is a schematic diagram of selecting a parameter
uniquely defining the cross-section in a cross-section of a human
leg in according to an exemplary embodiment of the present
disclosure.
DETAILED DESCRIPTION
[0063] Exemplary embodiments of the present disclosure will now be
described more fully with reference to accompanying drawings.
However, the exemplary embodiments herein are provided to assist in
the understanding of the disclosure and are not intended to limit
the disclosure in any way. On the contrary, the embodiments are
provided to make the description of the present disclosure more
comprehensive and complete, and to transmit the concept of the
exemplary embodiments to those skilled in the art. The drawings are
only schematic representations of the disclosure, and are not
necessarily to scale. The same reference numerals in the drawings
denote the same or similar parts, and the repeated description
thereof will be omitted.
[0064] Further, described features, structures, or advantages can
be combined in any suitable manner in one or more embodiments. In
the following description, numerous specific details are provided
to give a full understanding of the embodiments of the present
disclosure. However, one skilled in the art may appreciate that one
or more of the specific details can be omitted while practicing
technical solutions of the present disclosure, or using other
equivalent methods, methods, apparatuses and steps to substitute.
In other cases, well-known structures, methods, implementations, or
operations are not shown or described in detail to avoid
overwhelming attention and obscure various aspects of the present
disclosure.
[0065] Some of block diagrams shown in the drawings are functional
entities and do not necessarily correspond to physically or
logically independent entities. These functional entities can be
implemented in the form of software, or implemented in one or more
hardware modules or integrated circuits, or implemented in
different networks and/or processor devices and/or microcontroller
devices.
[0066] FIG. 6 is a structural diagram of a detection device of
intelligent bionic human body part model according to an exemplary
embodiment of the present disclosure.
[0067] The body part refers to the part on the human body that is
in contact with the pressure garment. For example, for pressure
stockings, the body parts that are in touched contact with the
pressure stockings are the legs. The bionic human body part model
refers to a bionic model of the human body part used in order to
detect pressure generated by the pressure garment on the human body
part when the pressure garment is manufactured. The detection
device of intelligent bionic human body part model is a complete
device that uses the bionic human body part model to detect the
pressure generated by the pressure garment on the human body part,
including the bionic human body part model and other supporting
units such as a detection unit used to detect the pressure
generated by the pressure garment on the human body part.
Intelligence means that the bionic human body part model may
monitor pressure distribution in real time, and may deform based on
different human bodies.
[0068] As shown in FIG. 6, the detection device of intelligent
bionic human body part model includes:
[0069] a bionic human body part model 1; and
[0070] a plurality of fiber grating sensing units 5 integrated on
an optical fiber 2 and a plurality of predetermined positions set
on the bionic human body part model 1.
[0071] The predetermined positions refer to preset positions on the
bionic human body part model to measure the pressure generated by
the pressure garment on the bionic human body part model, and can
be predefined by an operator. As shown in FIG. 6, the plurality of
fiber grating sensing units 5 are set at the plurality of
predetermined positions on the bionic human body part model 1 of
the detection device 98 of intelligent bionic human body part
model. Each optical fiber 2 is connected to the plurality of fiber
grating sensing units 5.
[0072] The principle of the fiber grating sensing unit for sensing
pressure is: taking Bragg fiber grating sensing as an example, when
light passes through the fiber grating, the fiber grating will
reflect or transmit a narrow spectrum component with a specific
wavelength as a center wavelength. At the same time, external
physical parameters (such as temperature, strain, etc.) may cause
wavelength shift.
[0073] For each fiber grating sensing unit, the pressure acting on
the surface can be transferred and converted into stretching of the
fiber grating. At the same time, the relationship between the
pressure/strain and the center wavelength of a reflected signal of
the fiber grating is established. Applying the relationship, the
pressure measurement can be performed by the fiber grating sensing
unit.
[0074] In embodiments of the present disclosure, the fiber grating
sensing unit is set at the plurality of predetermined positions on
the bionic human body part model, instead of setting the electronic
pressure sensor on the bionic human body part model, and these
fiber grating sensing units are integrated on the optical fiber,
that is, the fiber grating sensing network is adopted. The fiber
grating sensor network has the following advantages: high
measurement accuracy, strong anti-electromagnetic interference
ability, fast measurement speed, good reusability and large
information capacity (one same fiber may transmit multiple
signals). In addition, the fiber grating sensing unit also has
characteristics of light weight, small size, insulation,
durability, long-term stability and so on. Moreover, sensing of
each fiber grating sensing unit is independent of each other, and
mutual interference is very small, which can effectively
inhibit/isolate the transmission of internal fiber deformation and
external fiber deformation of the fiber grating sensing unit. In
related art, an electronic pressure sensor is applied to the bionic
human body part model, the signal generated under small pressure
does not change significantly, and its sensing response is the same
order of magnitude as the noise generated by the environment or
even smaller, so it is seriously interfered by external signals.
The embodiments of the present disclosure adopt the fiber grating
sensor network, which can effectively avoid the problem.
[0075] In an embodiment, as shown in FIG. 7, each of the plurality
of fiber grating sensing units 5 includes:
[0076] a substrate 8;
[0077] a groove 9 on the substrate 8; and
[0078] a Bragg grating 7, suspended on a surface of the groove 9,
wherein an air chamber is formed at the groove after packaging.
[0079] The advantage of using Bragg grating to fabricate fiber
grating sensing unit is that it can be free from electromagnetic
interference and has high sensitivity to axial stretch.
[0080] Since the air chamber is formed at the groove of the above
structure after packaging, it is also very easy to deform under
small pressure, thus showing a very high sensitivity (At present,
the pressure sensitivity in the range of 0-10 kPa is higher than 20
pm/kPa, and the pressure and wavelength shift are linear).
[0081] In an embodiment, a plurality of optical fiber sensing
channels are set on the bionic human body part model 1, the
plurality of optical fiber grating sensing units are dispersed in
each of the optical fiber sensing channels, at least one fiber
grating sensing unit is dispersed in one fiber optic sensing
channel, and at least one fiber grating sensing unit has different
center wavelengths. For example, 4 fiber grating sensor channels
are set on the bionic human body part model 1, and each channel has
7 fiber grating sensing units, and the center wavelengths of the
gratings of these 7 fiber grating sensing units are different from
each other. For example, each fiber grating sensor channel is a
fiber branch, and a plurality of fiber branches are juxtaposed to
form a unified fiber.
[0082] The above structure utilizes the principle of wavelength
division multiplexing, and uses fiber grating sensing units with
different central wavelengths in each optical fiber sensing channel
to measure the pressure distribution caused by garment on the
bionic human body part model. In each channel, a broad-spectrum
light source provides an input signal, through fiber gratings with
different center wavelengths integrated in one same fiber, and each
fiber grating reflects a narrow-band spectrum with different center
wavelengths.
[0083] In an embodiment, material used for manufacturing the
constructed bionic human body part model includes an elastomer with
a Young's modulus below 1 MPa. The advantage of using the elastomer
with a Young's modulus below 1 MPa is that it is similar to a
Young's modulus of skin, and it has better elastic recovery
performance and shape retention, convenient processing, and
reasonable cost.
[0084] In an embodiment, the elastomer with a Young's modulus below
1 MPa is one or more of following: silicone, polyurethane,
polyester, and polyacrylic acid.
[0085] In an embodiment, the detection device further includes:
[0086] a base 4 set at a bottom of the bionic human body part model
1;
[0087] a protective layer 4 integrally connected to an upper
surface of the base 4 for protecting the optical fiber.
[0088] As shown in FIG. 6, the base 4 is at the bottom of the
bionic human body part model 1, which can improve stability and
facilitate measurement. The protective layer 3 is integrally
connected to the base 4 to protect the optical fiber 2. Such a
structure can make the detection device 98 of intelligent bionic
human body part model not easily damaged.
[0089] In an embodiment, as shown in FIG. 7, a plurality of fiber
grating sensing units 5 are embedded in a soft elastic body 6
without protruding from the soft elastic body 6. The soft elastic
body 6 is set on the bionic human body part model.
[0090] The advantage of embedding the fiber grating sensing units 5
with the Bragg grating 7 in the elastic body 6 is that the elastic
body 6 is soft and flexible, and the elastic coefficient of the
optical fiber is 2-3 orders of magnitude larger than that of the
elastic body 6, so that the axial deformation of the optical fiber
under pressure is very small, and the sensitivity under small
pressure is relatively low. The fiber grating pressure sensing
units shown in FIG. 7 can just solve this problem.
[0091] In an embodiment, an interrogator 96 (for example, a light
sensor interrogator) detects that the fiber grating sensing units 5
output a detection result, and sends the detection result to a
processor 97 via Ethernet 11 for the processor to process the
pressure on the detected human body part model to obtain the
detection result.
[0092] In an embodiment, the optical fiber 2 is straight and fixed
to the substrate 8 by an adhesive 10 to increase pretension.
[0093] In an embodiment, the substrate 8 is a hard substrate, and
its elastic coefficient is similar to that of the optical fiber
2.
[0094] Since a detection unit of optical fiber pressure adopts the
above structure, the cross section is larger, and rigidity of the
substrate 8 is much higher than that of the optical fiber 2. In
this way, the hard substrate that produces negligible deformation
under the action of the pressure garment can avoid the interference
of the connected optical fiber, and effectively limit the
interference between different detection units of optical fiber
pressure connected to the same fiber. In addition, the advantage of
the groove is that air can be contained in the groove, which is
advantageous for improving the pressure sensitivity. Based on the
above design, there is a linear relationship between the wavelength
drift of the Bragg grating and the pressure of the bionic human
body part model, which improves repeatability of detection and
reduces hysteresis. The sensitivity of the detection units of
optical fiber pressure in the embodiments of the present disclosure
can reach 20 pm/kpa.
[0095] As shown in FIG. 1, in an embodiment, the bionic human body
part model is generated as following:
[0096] In step S101, a plurality of cross-sections are intercepted
for each human body part sample among a plurality of human body
part samples;
[0097] In step S102, a plurality of parameters are selected a on
each cross-section, wherein the plurality of parameters uniquely
defines the cross-section;
[0098] In step S103, a parameter matrix is constructed using a
plurality of parameters of a plurality of intercepted
cross-sections for each human body part sample of the plurality of
human body part samples;
[0099] In step S104, an average parameter matrix and a principal
component vector are calculated for the parameter matrices of the
plurality of human body part samples, wherein the average parameter
matrix represents an average body part shape and the principal
component vector represents a main difference of the human body
part; and
[0100] In step S107, the bionic human body part model is generated
based on the average parameter matrix, the principal component
vector, and at least one principal component measurement value on a
target human body part.
[0101] These steps are described in detail below.
[0102] In step S101, a plurality of cross-sections are intercepted
for each human body part sample among a plurality of human body
part samples.
[0103] Human body parts refer to parts on the human body, such as
human legs. The human body part samples refer to the human body
parts selected in advance for testing in order to obtain an average
human body part model and principal component vector. The concept
of the average human body part model and the principal component
vector will be described later. The more samples are selected, the
more accurate the test result is. For example, the test result of
selecting 1000 human legs as a sample is more accurate than the
test result of selecting 100 legs as a sample.
[0104] For each sample, the plurality of cross-sections are
intercepted. For example, a cross-section is intercepted every 0.05
meters in the height direction of the leg, and the cross-section
intercepted is shown in FIG. 8. The more cross-sections are
intercepted, the more accurate the measurement result is.
[0105] In step S102, a plurality of parameters are selected a on
each cross-section, wherein the plurality of parameters uniquely
defines the cross-section.
[0106] Parameters refer to variables that need to be used in order
to characterize the whole or part of the cross-sectional profile
shape. In an embodiment, the cross-section of the human leg is
shown in FIG. 8. The cross-section of the human leg is seen as a
small circle arc and a large circle arc connected by two straight
tangent lines. After the radius r of the small circle, the radius R
of the large circle, and lengths d1 and d2 of the two straight
tangent lines are determined, the cross-sectional profile shape is
uniquely determined. As long as one of these four variables is
uncertain, the profile shape of the cross-section is uncertain. The
radius r of the small circle, the radius R of the large circle, and
the lengths d1 and d2 of the two straight tangent lines are
selected parameters, which can uniquely define the
cross-section.
[0107] In step S103, a parameter matrix is constructed using a
plurality of parameters of a plurality of intercepted
cross-sections for each human body part sample of the plurality of
human body part samples.
[0108] The parameter matrix refers to a matrix composed of the
plurality of parameters of the plurality of intercepted
cross-sections for the human body part sample, wherein one of row
and column of the matrix represents the cross-section, the other
represents the parameter, and elements of the matrix represent
corresponding parameter values of corresponding cross-section. For
example, the row represents the cross-section, and the column
represents the parameter. The radius r of the small circle, the
radius R of the large circle, and the lengths d1 and d2 of the two
straight tangent lines are represented by columns 1-4,
respectively, and the element represents the radius R of the large
circle of the third cross-section.
[0109] In step S104, an average parameter matrix and a principal
component vector are calculated for the parameter matrices of the
plurality of human body part samples.
[0110] The average parameter matrix refers to a matrix obtained by
averaging the plurality of parameter matrices. Averaging the
plurality of parameter matrices refers to performing such
operations on the plurality of parameter matrices: averaging the
parameters at the same position (the row number and column number
are the same) of the plurality of parameter matrices as the
parameter at the same position of the average parameter matrix. The
average parameter matrix represents a level of each parameter on
each cross-section of an average human body part. Taking the
element in the third row and the second column above that
represents the radius R of the third cross-section of the large
circle as an example, the element in the third row and the second
column of the average parameter matrix represents the average level
of the radius R of the large circle of the third cross-section of
the human body part of a large number of people.
[0111] The principal component vector refers to a vector composed
of variance that is obtained by performing principal component
analysis on the parameter matrix of the plurality of human body
parts samples, extracting the most significant parameters of
different people in the parameter matrix and calculating the
variance. The principal component analysis is a commonly used
method in the field, it can extract the most significant element
composition. For example, the element in the third row and the
first column of the parameter matrix of the plurality of human body
part samples have very little difference among different people,
and the element in the third row and the second column of the
parameter matrix of the plurality of human body part samples vary
greatly among different people. This means that in different
people, the radius r of the small circle of the third cross-section
of the human body has very little difference, the radius R of the
large circle of the third cross-section of the human body is very
different, and the effect of using it to distinguish different
human body parts is obvious. At this time, the variances of the
element in the third row and second column of the parameter matrix
of the plurality of human body part samples are put into the
principal component vector as the principal component.
[0112] In an embodiment, the determining the principal component
vector for the parameter matrices of the plurality of human body
part samples specifically includes the following steps.
[0113] The variance matrix for the parameter matrices of the
multiple human body part samples is obtained; and
[0114] The principal component vector is determined based on the
variance matrix.
[0115] The variance matrix refers to a matrix obtained by
calculating the variance of the plurality of parameter matrices.
Calculating the variance of the plurality of parameter matrices
refers to performing such operations on the plurality of parameter
matrices: calculating the variance of the parameters at the same
position (the row number and column number are the same) of the
plurality of parameter matrices as the parameter at the same
position of the variance matrix. The variance at a specific
position of the variance matrix indicates the degree of difference
among different people in the parameters of the cross-section of
the human body part corresponding to the specific position. The
larger the variance at the specific position is, the larger the
difference in the parameters of the corresponding cross-section of
human body part among different people is. Therefore, the element
(variance) with the larger value in the variance matrix is
extracted to synthesis the principal component vector. Each
principal component in the principal component vector is the
variance extracted from the position with the larger variance in
the variance matrix. The position where the variance is relatively
large is the parameter with relatively large difference in the
corresponding parameter in the human body.
[0116] In step S107, the bionic human body part model is generated
based on an average parameter matrix, a principal component vector,
and at least one principal component measurement value on a target
human body part.
[0117] The target human body part refers to the human body part for
which the bionic human body part model is to be generated. For
example, a user wants to order stockings. After the stockings leave
the factory, it is not known whether the stockings are suitable for
the user. Therefore, it is necessary to generate a bionic leg model
for the user's leg, which is very close to the shape of the user's
leg. The stockings are worn on the bionic leg model, and it can be
checked whether the stockings are suitable for the user through the
pressure sensor on the bionic leg model. At this time, the user's
leg is the target body part.
[0118] The principal component measurement value on the target
human body part refers to the difference between the value obtained
by measuring the parameter with the most significant difference
among different people in the target human body part and the
average value of the parameter. The average value of the parameter
refers to the parameter value at the position of the parameter in
the average parameter matrix. For example, the principal component
vector includes the variance of the radius R of the large circle of
the third cross-section, the variance of the radius r of the small
circle of the fifth cross-section, and the variance of the radius r
of the small circle of the seventh cross-section. Assuming that the
variance of the radius R of the large circle of the third
cross-section is 4 cm, and the average value of the radius R of the
large circle of the third cross section is 5 cm, the radius R of
the large circle of the third cross-section of the target human leg
is measured to be 7 cm, then the principal component measurement
value of the radius R of the large circle corresponding to the
third cross-section of the target leg is 7-5=2 (cm).
[0119] As is shown in FIG. 2, in an embodiment, the step S107
specifically includes the following steps.
[0120] S1071: an average bionic human body part model based on the
average parameter matrix is generated; and
[0121] S1072: the average bionic human body part model is deformed
according to at least one principal component measurement value on
the target human body part and corresponding principal component
value in the principal component vector.
[0122] Since the average parameter matrix represents, for one
average human body part, the level of each parameter on each
cross-section, according to each parameter in the matrix, an
average bionic human body part model can be generated, which
represents the shape of the average human body part among all
people. For example, according to the average level of each
parameter on each cross-section of the human leg, an average human
leg model can be constructed, that is, the average bionic human
body part model, which reflects the average human leg shape. 3D
printing technology can be used to generate the average bionic
human body part model.
[0123] In an embodiment, as is shown in FIG. 3, the step S1072
specifically includes the following steps.
[0124] S10721: a ratio of at least one principal component
measurement value on the target human body part and the
corresponding principal component value in the principal component
vector is determined; and
[0125] S10722: the average bionic human body part model is deformed
according to product of the ratio and the principal component
vector.
[0126] The ratio of at least one principal component measurement
value on the target human body part and the corresponding principal
component value in the principal component vector represents that,
for the parameter corresponding to the at least one principal
component measurement value, the degree to which the value of the
parameter of the target human body part deviates from the average
level of the parameter value (that is, the parameter value of the
corresponding position in the average parameter matrix) in the
entire population distribution. The higher the ratio is, the more
severe the deviation of the parameter of the target human body part
from the average level in the entire population distribution is.
For example, the probability that the deviation from the mean is
less than or equal to the variance is 68.5%, that is, 68.5% of
people's parameter value will fall within the interval of
(mean-variance, mean+variance). If the ratio of at least one
principal component measurement value on the target human body part
to the corresponding principal component value in the principal
component vector is 3, then 99% of people's parameter value will
fall within (mean-3 times the variance, mean+3 times the variance),
indicating that the value of this parameter is far from the average
level of this parameter.
[0127] Since each human body part is proportional, the ratio of at
least one principal component measurement value on the target human
body part and the corresponding principal component value in the
principal component vector also represents the ratio of other
principal component measurement values on the target human body
part to the corresponding principal component values in the
principal component vector.
[0128] For example, the principal component vector has the radius R
of the large circle of the third cross-section, the radius r of the
small circle of the fifth cross-section, and the radius r of the
small circle of the seventh cross-section. The average value of the
radius R of the large circle of the third cross-section is 5 cm,
and the variance is 2 cm. The radius R of the large circle of the
third cross-section on the leg of the target user is measured to be
6 cm. The ratio of the measurement value of the principal component
corresponding to the radius R of the large circle of the third
cross-section on the user's leg to the corresponding principal
component value in the principal component vector is (6-5)/2=0.5.
The average value of the radius r of the small circle of the fifth
cross-section is 3 cm, and the variance is 1 cm. The product of the
ratio of 0.5 and the variance of 1 cm of the radius r of the small
circle of the fifth cross-section is 0.5 cm, then the radius r of
the small circle of the fifth cross-section of the average bionic
human body part model is deformed according to 0.5 cm. The average
value of the radius r of the small circle of the seventh
cross-section is 2 cm, and the variance is 0.4 cm. The product of
the ratio of 0.5 and the radius of the small circle of the seventh
cross-section r and the variance of 0.4 cm is 0.2 cm, then the
radius r of the small circle of the seventh cross-section of the
average bionic human body part model is deformed according to 0.2
cm.
[0129] The way of deformation can be carried out by inflating or
deflating the average bionic human body part model, etc., or can be
other mechanical driving methods, or can be carried out by other
methods known to those skilled in the art.
[0130] As shown in FIG. 4, in an embodiment, before step S107, the
method further includes the following steps.
[0131] S105: a three-dimensional image of the target human body
part is obtained; and
[0132] S105: at least one principal component measurement value on
the target human body part is obtained from the three-dimensional
image.
[0133] The three-dimensional image of the human body part refers to
an image of the human body part that extends in three dimensions
(for example, x-axis, y-axis, and z-axis) in a three-dimensional
coordinate system.
[0134] In an embodiment, step S105 can be implemented by 3D
scanning.
[0135] In step S106, since the three-dimensional image of the
target human body part is obtained, at least one principal
component measurement value on the target human body part can be
obtained in the three-dimensional image. For example, from the
captured three-dimensional image of the user's human leg, it can be
obtained that the radius R of the large circle of the third
cross-section of the human leg minus the average value of the
radius R of the great circle of the third cross-section is the
corresponding principal component measurement value.
[0136] In the embodiments of the present disclosure, instead of
simply defining human body parts (such as human legs) through
simple measurement values (such as thigh length, calf length, knee
diameter, and foot length), the plurality of cross-sections of
human body parts (such as different heights of cross-sections of
legs) are intercepted, the plurality of parameters are selected on
each cross-section to uniquely define the cross-section (for
example, the cross-section of the leg is regarded as a small circle
arc and a large circle arc connected by two straight tangent lines,
and the radius of the small circle, the radius of the large circle,
and the length of each of the two straight tangent lines may
uniquely define the cross-section). In this way, the various
parameters selected on each cross-section are combined to form the
parameter matrix. Compared with the simple measurement values (such
as thigh length, calf length, knee diameter, and foot length) of
the related art, the matrix may more effectively capture and
represent the shape details of human body parts. The average
parameter matrix and the principal component vector for the
parameter matrix of a large number of human body part samples are
calculated in the embodiments of the present disclosure, where the
average parameter matrix represents the average body part shape,
and the principal component vector represents the main difference
of the human body part. In this way, the average parameter matrix
corresponds to an average human body part (each parameter of each
cross-section of the human body part takes the average value of the
corresponding parameter of the corresponding cross-section of the
large number of human body part samples). Through the principal
component vector, it can be known that which parameters are
dominant among all the parameters of all cross-sections of the
human body part, which can reflect the differences of different
people and the level of change between different people for the
parameter. Then, according to at least one principal component
measurement value on the target human body part, it can be known
that the position of the target human body part of the target
person in all the population in terms of the principal component,
and then it can be known that how much deformation of the average
human body part model corresponding to the average parameter matrix
can be made to be close to the real shape of the target human body
part. Therefore, the detection device of intelligent bionic human
body part model manufactured by this method is more accurate in
detecting the pressure of the pressure garment on the human body
part, and reduces the pressure measurement result of the pressure
garment on the human leg. And the difference between the real
pressure generated by pressure garment on human legs.
[0137] As shown in FIG. 4, in one embodiment, the method further
includes step S108, a plurality of fiber grating sensing units
integrated on an optical fiber are set at a plurality of
predetermined positions of the bionic human body part model.
[0138] As shown in FIG. 4, in an embodiment, the method further
includes the following steps.
[0139] In step S109, a base at the bottom of the bionic human body
part model is set; and
[0140] In step S110, a protective layer to an upper surface of the
base for protecting the optical fiber is integrally connected.
[0141] As shown in FIG. 5, in an embodiment, each of the plurality
of fiber grating sensing units is constructed as follows:
[0142] In step S201, a substrate is set;
[0143] In step S202, a groove on the substrate is etched; and
[0144] In step S203, a Bragg grating on a surface of the groove is
suspended, wherein an air chamber is formed at the groove after
packaging.
[0145] Those skilled in the art will easily think of other
embodiments of the present disclosure after considering the
specification and practicing the invention disclosed herein. The
present disclosure is intended to cover any variations, uses, or
adaptive changes of the present disclosure. These variations, uses,
or adaptive changes follow the general principles of the present
disclosure and include common knowledge or conventional technical
means in the technical field not disclosed in the present
disclosure. The description and the embodiments are to be regarded
as exemplary only, and the true scope and spirit of the present
disclosure are pointed out by the following claims.
[0146] It should be understood that the present disclosure is not
limited to the precise structure that has been described above and
shown in the drawings, and various modifications and changes can be
made without departing from its scope. The scope of the present
disclosure is limited only by the appended claims.
* * * * *